Visualizing and understanding convolutional networks

Matthew D. Zeiler, Rob Fergus

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark Krizhevsky et al. [18]. However there is no clear understanding of why they perform so well, or how they might be improved. In this paper we explore both issues. We introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. Used in a diagnostic role, these visualizations allow us to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark. We also perform an ablation study to discover the performance contribution from different model layers. We show our ImageNet model generalizes well to other datasets: when the softmax classifier is retrained, it convincingly beats the current state-of-the-art results on Caltech-101 and Caltech-256 datasets.

Original languageEnglish (US)
Title of host publicationComputer Vision, ECCV 2014 - 13th European Conference, Proceedings
PublisherSpringer Verlag
Pages818-833
Number of pages16
Volume8689 LNCS
EditionPART 1
ISBN (Print)9783319105895
DOIs
StatePublished - 2014
Event13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland
Duration: Sep 6 2014Sep 12 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8689 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other13th European Conference on Computer Vision, ECCV 2014
CountrySwitzerland
CityZurich
Period9/6/149/12/14

Fingerprint

Visualization
Classifier
Benchmark
Classifiers
Ablation
Beat
Network Model
Diagnostics
Model
Generalise
Architecture

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In Computer Vision, ECCV 2014 - 13th European Conference, Proceedings (PART 1 ed., Vol. 8689 LNCS, pp. 818-833). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8689 LNCS, No. PART 1). Springer Verlag. https://doi.org/10.1007/978-3-319-10590-1_53

Visualizing and understanding convolutional networks. / Zeiler, Matthew D.; Fergus, Rob.

Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. Vol. 8689 LNCS PART 1. ed. Springer Verlag, 2014. p. 818-833 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8689 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zeiler, MD & Fergus, R 2014, Visualizing and understanding convolutional networks. in Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. PART 1 edn, vol. 8689 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 8689 LNCS, Springer Verlag, pp. 818-833, 13th European Conference on Computer Vision, ECCV 2014, Zurich, Switzerland, 9/6/14. https://doi.org/10.1007/978-3-319-10590-1_53
Zeiler MD, Fergus R. Visualizing and understanding convolutional networks. In Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. PART 1 ed. Vol. 8689 LNCS. Springer Verlag. 2014. p. 818-833. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). https://doi.org/10.1007/978-3-319-10590-1_53
Zeiler, Matthew D. ; Fergus, Rob. / Visualizing and understanding convolutional networks. Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. Vol. 8689 LNCS PART 1. ed. Springer Verlag, 2014. pp. 818-833 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).
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